






import glob
import os
import random
from string import ascii_lowercase, digits

import cva
UpperCAmelCase__      			=						""
UpperCAmelCase__      			=						""
UpperCAmelCase__      			=						""
UpperCAmelCase__      			=						1  # (0 is vertical, 1 is horizontal)







def     a_   ()				->						Any:



					"""simple docstring"""
					__a					:						Dict      =					get_dataset(lowerCamelCase_				,       lowerCamelCase_      )
					print("Processing..."      )
					__a					:						List[Any]      =					update_image_and_anno(lowerCamelCase_				,       lowerCamelCase_				,       lowerCamelCase_      )

					for index, image in enumerate(lowerCamelCase_      ):
										# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
										__a					:						int      =					random_chars(32      )
										__a					:						Optional[int]      =					paths[index].split(os.sep      )[-1].rsplit("."				,       1      )[0]
										__a					:						Optional[int]      =					f'{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'
										cva.imwrite(f'/{file_root}.jpg'				,       lowerCamelCase_				,       [cva.IMWRITE_JPEG_QUALITY, 85]      )
										print(f'Success {index+1}/{len(lowerCamelCase_      )} with {file_name}'      )
										__a					:						Any      =					[]
										for anno in new_annos[index]:
															__a					:						Tuple      =					f'{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'
															annos_list.append(lowerCamelCase_      )
										with open(f'/{file_root}.txt'				,       "w"      ) as outfile:
															outfile.write("\n".join(line for line in annos_list      )      )







def     a_   (__A				,       __A      )				->						List[str]:



					"""simple docstring"""
					__a					:						Any      =					[]
					__a					:						List[Any]      =					[]
					for label_file in glob.glob(os.path.join(lowerCamelCase_				,       "*.txt"      )      ):
										__a					:						Tuple      =					label_file.split(os.sep      )[-1].rsplit("."				,       1      )[0]
										with open(lowerCamelCase_      ) as in_file:
															__a					:						str      =					in_file.readlines()
										__a					:						Optional[int]      =					os.path.join(lowerCamelCase_				,       f'{label_name}.jpg'      )

										__a					:						Union[str, Any]      =					[]
										for obj_list in obj_lists:
															__a					:						str      =					obj_list.rstrip("\n"      ).split(" "      )
															boxes.append(
															    [
															        int(obj[0]      ),
															        float(obj[1]      ),
															        float(obj[2]      ),
															        float(obj[3]      ),
															        float(obj[4]      ),
															    ]      )
										if not boxes:
															continue
										img_paths.append(lowerCamelCase_      )
										labels.append(lowerCamelCase_      )
					return img_paths, labels







def     a_   (__A				,       __A				,       __A = 1      )				->						int:



					"""simple docstring"""
					__a					:						Dict      =					[]
					__a					:						str      =					[]
					__a					:						Optional[int]      =					[]
					for idx in range(len(lowerCamelCase_      )      ):
										__a					:						Any      =					[]
										__a					:						Optional[int]      =					img_list[idx]
										path_list.append(lowerCamelCase_      )
										__a					:						int      =					anno_list[idx]
										__a					:						Optional[int]      =					cva.imread(lowerCamelCase_      )
										if flip_type == 1:
															__a					:						List[str]      =					cva.flip(lowerCamelCase_				,       lowerCamelCase_      )
															for bbox in img_annos:
																				__a					:						Tuple      =					1 - bbox[1]
																				new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]      )
										elif flip_type == 0:
															__a					:						List[str]      =					cva.flip(lowerCamelCase_				,       lowerCamelCase_      )
															for bbox in img_annos:
																				__a					:						int      =					1 - bbox[2]
																				new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]      )
										new_annos_lists.append(lowerCamelCase_      )
										new_imgs_list.append(lowerCamelCase_      )
					return new_imgs_list, new_annos_lists, path_list







def     a_   (__A = 32      )				->						Dict:



					"""simple docstring"""
					assert number_char > 1, "The number of character should greater than 1"
					__a					:						List[Any]      =					ascii_lowercase + digits
					return "".join(random.choice(lowerCamelCase_      ) for _ in range(lowerCamelCase_      )      )


if __name__ == "__main__":
			main()
			print('''DONE ✅''')








import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


lowercase			:				Optional[int]   =     logging.get_logger(__name__)

lowercase			:				Optional[Any]   =     {"vocab_file": "vocab.txt"}

lowercase			:				Optional[int]   =     {
    "vocab_file": {
        "YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt",
        "YituTech/conv-bert-medium-small": (
            "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt"
        ),
        "YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt",
    }
}

lowercase			:				Tuple   =     {
    "YituTech/conv-bert-base": 5_12,
    "YituTech/conv-bert-medium-small": 5_12,
    "YituTech/conv-bert-small": 5_12,
}


lowercase			:				Optional[int]   =     {
    "YituTech/conv-bert-base": {"do_lower_case": True},
    "YituTech/conv-bert-medium-small": {"do_lower_case": True},
    "YituTech/conv-bert-small": {"do_lower_case": True},
}







class 					__lowercase  (						_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		int								=	VOCAB_FILES_NAMES
							UpperCAmelCase_						:		Optional[int]								=	PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase_						:		List[Any]								=	PRETRAINED_INIT_CONFIGURATION
							UpperCAmelCase_						:		Optional[Any]								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase_						:		Optional[int]								=	ConvBertTokenizer




							def __init__(					self    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=True    ,						__UpperCAmelCase="[UNK]"    ,						__UpperCAmelCase="[SEP]"    ,						__UpperCAmelCase="[PAD]"    ,						__UpperCAmelCase="[CLS]"    ,						__UpperCAmelCase="[MASK]"    ,						__UpperCAmelCase=True    ,						__UpperCAmelCase=None    ,						**__UpperCAmelCase    ,						)		->       Optional[int]:
														super().__init__(
														    __UpperCAmelCase    ,						tokenizer_file=__UpperCAmelCase    ,						do_lower_case=__UpperCAmelCase    ,						unk_token=__UpperCAmelCase    ,						sep_token=__UpperCAmelCase    ,						pad_token=__UpperCAmelCase    ,						cls_token=__UpperCAmelCase    ,						mask_token=__UpperCAmelCase    ,						tokenize_chinese_chars=__UpperCAmelCase    ,						strip_accents=__UpperCAmelCase    ,						**__UpperCAmelCase    ,						)

														A      :					Any      		=					json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
														if (
														    normalizer_state.get('''lowercase'''    ,						__UpperCAmelCase						) != do_lower_case
														    or normalizer_state.get('''strip_accents'''    ,						__UpperCAmelCase						) != strip_accents
														    or normalizer_state.get('''handle_chinese_chars'''    ,						__UpperCAmelCase						) != tokenize_chinese_chars
														):
																					A      :					Tuple      		=					getattr(__UpperCAmelCase    ,						normalizer_state.pop('''type'''						)						)
																					A      :					int      		=					do_lower_case
																					A      :					int      		=					strip_accents
																					A      :					Union[str, Any]      		=					tokenize_chinese_chars
																					A      :					str      		=					normalizer_class(**__UpperCAmelCase						)

														A      :					str      		=					do_lower_case




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase=None						)		->       Optional[int]:
														A      :					Dict      		=					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

														if token_ids_a:
																					output += token_ids_a + [self.sep_token_id]

														return output




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None						)		->       List[int]:
														A      :					List[Any]      		=					[self.sep_token_id]
														A      :					Optional[Any]      		=					[self.cls_token_id]
														if token_ids_a is None:
																					return len(cls + token_ids_a + sep						) * [0]
														return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]






							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None						)		->       Tuple[str]:
														A      :					Optional[int]      		=					self._tokenizer.model.save(__UpperCAmelCase    ,						name=__UpperCAmelCase						)
														return tuple(__UpperCAmelCase						)






